Table 1.

Examples of text segments and their coding as “drivers” or “obstacles.”

Coding	Sample text segments
Drivers (positive influences on adoption)	With more tools available and importantly more experienced practitioners of the art of interpretation forthcoming it is most likely that environmental science will increasingly experience the application of genomic tools in chemical assessments (ECETOC 2007 p. 19).
	Industry, government, and academic institutions all are engaged in developing and applying omic data. The strongest driver behind the development of these technologies is the pharmaceutical industry, which is confident that these techniques will accelerate drug discovery and toxicity testing (Balbus and Environmental Defense 2005 p. 12).
Obstacles (negative influences on adoption)	Without a clearly defined approach to categorize in vitro effects as beneficial, adverse, or irrelevant (normal variation), there is the concern that pathway perturbation results will not be credible as a risk assessment tool for the regulatory community (Andersen and Krewski 2010 p. 19).
	Data sharing and providing adequate informatics support to retrieve and utilise available data, including those from NAMs [new approach methodologies], is a key challenge to supporting their use for regulatory purposes (ECHA 2016 p. 13).

Note: Underlined phrases in this table are the summary labels for each driver which are used in the rest of this article for ease of reference.